From the 1 of 6 linked papers with an AI index.
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Towards Vision-Free CIR: Attribute-Augmented Scoring and LLM-Based Reranking for Zero-Shot Composed Image Retrieval
Ryotaro Shimada, Yu-Chieh Lin, Yuji Nozawa +3
The paper proposes a vision‑free framework for composed image retrieval that uses attribute‑augmented scoring to recover visual details and a large language model for reranking to…
CIRCLED: A Multi-turn CIR Dataset with Consistent Dialogues across Domains
Tomohisa Takeda, Yu-Chieh Lin, Yuji Nozawa +3
Existing Multi-Turn Composed Image Retrieval (MTCIR) datasets lack dialogue-historyconsistency and are restricted to the fashion domain. To address these limitations, we construct…
Prompt-Guided Attention Head Selection for Focus-Oriented Image Retrieval
Yuji Nozawa, Yu-Chieh Lin, Kazumoto Nakamura +1
The goal of this paper is to enhance pretrained Vision Transformer (ViT) models for focus-oriented image retrieval with visual prompting. In real-world image retrieval scenarios, b…
Improving Image Clustering with Artifacts Attenuation via Inference-Time Attention Engineering
Kazumoto Nakamura, Yuji Nozawa, Yu-Chieh Lin +2
The goal of this paper is to improve the performance of pretrained Vision Transformer (ViT) models, particularly DINOv2, in image clustering task without requiring re-training or f…
Revisiting Relevance Feedback for CLIP-based Interactive Image Retrieval
Ryoya Nara, Yu-Chieh Lin, Yuji Nozawa +4
Many image retrieval studies use metric learning to train an image encoder. However, metric learning cannot handle differences in users' preferences, and requires data to train an…